Overview

Dataset statistics

Number of variables13
Number of observations1989
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.2 KiB
Average record size in memory155.1 B

Variable types

NUM12
CAT1

Reproduction

Analysis started2020-09-30 11:24:42.357588
Analysis finished2020-09-30 11:25:57.474158
Duration1 minute and 15.12 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

state has a high cardinality: 51 distinct values High cardinality
Aggravated assault is highly correlated with violent totalHigh correlation
violent total is highly correlated with Aggravated assaultHigh correlation
Larceny theft is highly correlated with property totalHigh correlation
property total is highly correlated with Larceny theftHigh correlation
state is uniformly distributed Uniform

Variables

state
Categorical

HIGH CARDINALITY
UNIFORM

Distinct count51
Unique (%)2.6%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
NM
 
39
PA
 
39
IA
 
39
MA
 
39
VA
 
39
Other values (46)
1794
ValueCountFrequency (%) 
NM392.0%
 
PA392.0%
 
IA392.0%
 
MA392.0%
 
VA392.0%
 
MD392.0%
 
AK392.0%
 
KS392.0%
 
WV392.0%
 
WA392.0%
 
OK392.0%
 
SD392.0%
 
TX392.0%
 
NY392.0%
 
NC392.0%
 
MT392.0%
 
CA392.0%
 
TN392.0%
 
NJ392.0%
 
UT392.0%
 
AR392.0%
 
AZ392.0%
 
NV392.0%
 
MI392.0%
 
VT392.0%
 
Other values (26)101451.0%
 
2020-09-30T12:25:57.761863image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters24
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A46811.8%
 
N42910.8%
 
M3518.8%
 
I3127.8%
 
C2345.9%
 
T2345.9%
 
D2345.9%
 
O1954.9%
 
L1563.9%
 
K1563.9%
 
S1563.9%
 
V1563.9%
 
W1563.9%
 
R1172.9%
 
E1172.9%
 
H1172.9%
 
Y1172.9%
 
Z391.0%
 
F391.0%
 
G391.0%
 
J391.0%
 
P391.0%
 
X391.0%
 
U391.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter3978100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A46811.8%
 
N42910.8%
 
M3518.8%
 
I3127.8%
 
C2345.9%
 
T2345.9%
 
D2345.9%
 
O1954.9%
 
L1563.9%
 
K1563.9%
 
S1563.9%
 
V1563.9%
 
W1563.9%
 
R1172.9%
 
E1172.9%
 
H1172.9%
 
Y1172.9%
 
Z391.0%
 
F391.0%
 
G391.0%
 
J391.0%
 
P391.0%
 
X391.0%
 
U391.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3978100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A46811.8%
 
N42910.8%
 
M3518.8%
 
I3127.8%
 
C2345.9%
 
T2345.9%
 
D2345.9%
 
O1954.9%
 
L1563.9%
 
K1563.9%
 
S1563.9%
 
V1563.9%
 
W1563.9%
 
R1172.9%
 
E1172.9%
 
H1172.9%
 
Y1172.9%
 
Z391.0%
 
F391.0%
 
G391.0%
 
J391.0%
 
P391.0%
 
X391.0%
 
U391.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3978100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A46811.8%
 
N42910.8%
 
M3518.8%
 
I3127.8%
 
C2345.9%
 
T2345.9%
 
D2345.9%
 
O1954.9%
 
L1563.9%
 
K1563.9%
 
S1563.9%
 
V1563.9%
 
W1563.9%
 
R1172.9%
 
E1172.9%
 
H1172.9%
 
Y1172.9%
 
Z391.0%
 
F391.0%
 
G391.0%
 
J391.0%
 
P391.0%
 
X391.0%
 
U391.0%
 

unemployment
Real number (ℝ≥0)

Distinct count827
Unique (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.127662141779789
Minimum2.3
Maximum17.825
Zeros0
Zeros (%)0.0%
Memory size15.7 KiB
2020-09-30T12:25:58.061576image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2.3
5-th percentile3.233
Q14.617
median5.8
Q37.325
95-th percentile9.9968
Maximum17.825
Range15.525
Interquartile range (IQR)2.708

Descriptive statistics

Standard deviation2.080685911
Coefficient of variation (CV)0.3395562391
Kurtosis1.126325452
Mean6.127662142
Median Absolute Deviation (MAD)1.325
Skewness0.8728270014
Sum12187.92
Variance4.329253861
2020-09-30T12:25:58.354785image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4.25890.5%
 
4.29290.5%
 
5.03380.4%
 
5.39280.4%
 
5.29270.4%
 
4.41770.4%
 
6.23370.4%
 
5.870.4%
 
4.970.4%
 
5.16770.4%
 
5.23370.4%
 
4.94270.4%
 
5.53370.4%
 
4.52570.4%
 
7.53360.3%
 
6.17560.3%
 
5.360.3%
 
6.61760.3%
 
5.560.3%
 
3.52560.3%
 
4.56760.3%
 
4.30860.3%
 
4.84260.3%
 
6.24260.3%
 
5.5560.3%
 
Other values (802)181991.5%
 
ValueCountFrequency (%) 
2.310.1%
 
2.31710.1%
 
2.3510.1%
 
2.39210.1%
 
2.410.1%
 
2.45810.1%
 
2.49210.1%
 
2.51710.1%
 
2.55810.1%
 
2.56710.1%
 
ValueCountFrequency (%) 
17.82510.1%
 
15.38310.1%
 
14.810.1%
 
14.36710.1%
 
14.110.1%
 
13.98310.1%
 
13.80810.1%
 
13.65810.1%
 
13.52510.1%
 
13.13310.1%
 

year
Real number (ℝ≥0)

Distinct count39
Unique (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1995.0
Minimum1976
Maximum2014
Zeros0
Zeros (%)0.0%
Memory size15.7 KiB
2020-09-30T12:25:58.650995image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1976
5-th percentile1977
Q11985
median1995
Q32005
95-th percentile2013
Maximum2014
Range38
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.25745896
Coefficient of variation (CV)0.005642836573
Kurtosis-1.201582316
Mean1995
Median Absolute Deviation (MAD)10
Skewness0
Sum3968055
Variance126.7303823
2020-09-30T12:25:58.950207image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2014512.6%
 
1985512.6%
 
1992512.6%
 
1991512.6%
 
1990512.6%
 
1989512.6%
 
1988512.6%
 
1987512.6%
 
1986512.6%
 
1984512.6%
 
1994512.6%
 
1983512.6%
 
1982512.6%
 
1981512.6%
 
1980512.6%
 
1979512.6%
 
1978512.6%
 
1977512.6%
 
1993512.6%
 
1995512.6%
 
2013512.6%
 
2005512.6%
 
2012512.6%
 
2011512.6%
 
2010512.6%
 
Other values (14)71435.9%
 
ValueCountFrequency (%) 
1976512.6%
 
1977512.6%
 
1978512.6%
 
1979512.6%
 
1980512.6%
 
1981512.6%
 
1982512.6%
 
1983512.6%
 
1984512.6%
 
1985512.6%
 
ValueCountFrequency (%) 
2014512.6%
 
2013512.6%
 
2012512.6%
 
2011512.6%
 
2010512.6%
 
2009512.6%
 
2008512.6%
 
2007512.6%
 
2006512.6%
 
2005512.6%
 

Population
Real number (ℝ≥0)

Distinct count1891
Unique (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5215378.300653595
Minimum382000
Maximum38802500
Zeros0
Zeros (%)0.0%
Memory size15.7 KiB
2020-09-30T12:25:59.258425image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum382000
5-th percentile592595.8
Q11316807
median3520355
Q36098000
95-th percentile17808200
Maximum38802500
Range38420500
Interquartile range (IQR)4781193

Descriptive statistics

Standard deviation5815879.894
Coefficient of variation (CV)1.115140563
Kurtosis8.627497874
Mean5215378.301
Median Absolute Deviation (MAD)2308818
Skewness2.604579271
Sum1.037338744e+10
Variance3.382445894e+13
2020-09-30T12:25:59.541127image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
118700030.2%
 
93500030.2%
 
58900030.2%
 
68900030.2%
 
58200030.2%
 
331700030.2%
 
71500030.2%
 
57000030.2%
 
66000030.2%
 
160600030.2%
 
99800030.2%
 
48000020.1%
 
40600020.1%
 
70200020.1%
 
327500020.1%
 
68500020.1%
 
195100020.1%
 
111100020.1%
 
262500020.1%
 
63600020.1%
 
1075200020.1%
 
486000020.1%
 
100500020.1%
 
62300020.1%
 
68600020.1%
 
Other values (1866)192896.9%
 
ValueCountFrequency (%) 
38200010.1%
 
39000010.1%
 
40300010.1%
 
40600020.1%
 
40700010.1%
 
41200010.1%
 
42400010.1%
 
43800010.1%
 
44014210.1%
 
45000010.1%
 
ValueCountFrequency (%) 
3880250010.1%
 
3843139310.1%
 
3799987810.1%
 
3768393310.1%
 
3733819810.1%
 
3696166410.1%
 
3675666610.1%
 
3655321510.1%
 
3645754910.1%
 
3615414710.1%
 

violent total
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count1739
Unique (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean464.79567621920563
Minimum47.0
Maximum2921.8
Zeros0
Zeros (%)0.0%
Memory size15.7 KiB
2020-09-30T12:25:59.838836image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile131.74
Q1275.5
median412.3
Q3590
95-th percentile940.14
Maximum2921.8
Range2874.8
Interquartile range (IQR)314.5

Descriptive statistics

Standard deviation295.7602301
Coefficient of variation (CV)0.6363231097
Kurtosis13.61351546
Mean464.7956762
Median Absolute Deviation (MAD)148
Skewness2.672339092
Sum924478.6
Variance87474.11368
2020-09-30T12:26:00.147555image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
305.240.2%
 
285.740.2%
 
457.530.2%
 
374.930.2%
 
275.730.2%
 
43130.2%
 
270.130.2%
 
272.530.2%
 
280.430.2%
 
28230.2%
 
42730.2%
 
52630.2%
 
256.830.2%
 
427.330.2%
 
446.130.2%
 
514.630.2%
 
28730.2%
 
33430.2%
 
377.330.2%
 
406.430.2%
 
254.630.2%
 
262.230.2%
 
284.620.1%
 
249.720.1%
 
236.820.1%
 
Other values (1714)191596.3%
 
ValueCountFrequency (%) 
4710.1%
 
51.310.1%
 
53.610.1%
 
53.710.1%
 
5410.1%
 
56.810.1%
 
59.110.1%
 
61.310.1%
 
61.810.1%
 
63.210.1%
 
ValueCountFrequency (%) 
2921.810.1%
 
2832.810.1%
 
2662.610.1%
 
2661.410.1%
 
2469.810.1%
 
2458.210.1%
 
2453.310.1%
 
2274.810.1%
 
2141.910.1%
 
2123.110.1%
 

Murder
Real number (ℝ≥0)

Distinct count198
Unique (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.651784816490698
Minimum0.2
Maximum80.6
Zeros0
Zeros (%)0.0%
Memory size15.7 KiB
2020-09-30T12:26:00.491299image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1.6
Q13.2
median5.4
Q38.4
95-th percentile13.2
Maximum80.6
Range80.4
Interquartile range (IQR)5.2

Descriptive statistics

Standard deviation6.560408282
Coefficient of variation (CV)0.9862628547
Kurtosis52.58305392
Mean6.651784816
Median Absolute Deviation (MAD)2.5
Skewness6.007055677
Sum13230.4
Variance43.03895682
2020-09-30T12:26:00.890083image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2361.8%
 
2.9351.8%
 
1.8311.6%
 
6.2301.5%
 
2.3291.5%
 
4.8291.5%
 
3.6291.5%
 
4.4281.4%
 
3.3281.4%
 
2.2281.4%
 
4271.4%
 
3271.4%
 
4.6261.3%
 
5.8251.3%
 
2.4251.3%
 
5.1251.3%
 
1.9251.3%
 
5.4241.2%
 
2.8241.2%
 
2.5241.2%
 
4.9241.2%
 
5.7241.2%
 
3.1241.2%
 
6.1241.2%
 
6.6241.2%
 
Other values (173)131466.1%
 
ValueCountFrequency (%) 
0.210.1%
 
0.630.2%
 
0.720.1%
 
0.830.2%
 
0.980.4%
 
160.3%
 
1.180.4%
 
1.2100.5%
 
1.3130.7%
 
1.4191.0%
 
ValueCountFrequency (%) 
80.610.1%
 
78.510.1%
 
77.810.1%
 
75.210.1%
 
73.110.1%
 
71.910.1%
 
7010.1%
 
6510.1%
 
59.510.1%
 
56.910.1%
 

rape
Real number (ℝ≥0)

Distinct count533
Unique (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.014027149321265
Minimum5.6
Maximum102.2
Zeros0
Zeros (%)0.0%
Memory size15.7 KiB
2020-09-30T12:26:01.285364image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum5.6
5-th percentile16.14
Q124.9
median31.7
Q341.3
95-th percentile58.42
Maximum102.2
Range96.6
Interquartile range (IQR)16.4

Descriptive statistics

Standard deviation13.54026707
Coefficient of variation (CV)0.3980789164
Kurtosis2.386196021
Mean34.01402715
Median Absolute Deviation (MAD)8.1
Skewness1.165389946
Sum67653.9
Variance183.3388323
2020-09-30T12:26:01.594582image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
27.1160.8%
 
26.9130.7%
 
20.3130.7%
 
29.7120.6%
 
41.2110.6%
 
29110.6%
 
28.2110.6%
 
25.8110.6%
 
33110.6%
 
28.9100.5%
 
24.6100.5%
 
26.8100.5%
 
27100.5%
 
20.6100.5%
 
34.3100.5%
 
27.8100.5%
 
29.1100.5%
 
18.7100.5%
 
33.4100.5%
 
24.9100.5%
 
31100.5%
 
21.6100.5%
 
27.4100.5%
 
29.2100.5%
 
32.1100.5%
 
Other values (508)172086.5%
 
ValueCountFrequency (%) 
5.610.1%
 
7.310.1%
 
8.210.1%
 
8.510.1%
 
8.710.1%
 
8.910.1%
 
910.1%
 
9.420.1%
 
9.510.1%
 
9.720.1%
 
ValueCountFrequency (%) 
102.210.1%
 
101.510.1%
 
98.610.1%
 
93.310.1%
 
91.810.1%
 
91.610.1%
 
89.110.1%
 
88.110.1%
 
86.510.1%
 
85.810.1%
 

Robbery
Real number (ℝ≥0)

Distinct count1397
Unique (%)70.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.6974358974359
Minimum6.4
Maximum1635.1
Zeros0
Zeros (%)0.0%
Memory size15.7 KiB
2020-09-30T12:26:01.933323image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum6.4
5-th percentile16.44
Q167.1
median112.5
Q3169.2
95-th percentile348.3
Maximum1635.1
Range1628.7
Interquartile range (IQR)102.1

Descriptive statistics

Standard deviation147.9273633
Coefficient of variation (CV)1.043966409
Kurtosis26.24778877
Mean141.6974359
Median Absolute Deviation (MAD)50.2
Skewness4.22908504
Sum281836.2
Variance21882.50483
2020-09-30T12:26:02.206517image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
111.660.3%
 
17.960.3%
 
13.650.3%
 
153.150.3%
 
80.940.2%
 
132.740.2%
 
18.640.2%
 
67.140.2%
 
109.540.2%
 
70.740.2%
 
17.140.2%
 
83.240.2%
 
16.240.2%
 
152.440.2%
 
135.640.2%
 
46.940.2%
 
79.640.2%
 
99.140.2%
 
92.140.2%
 
1540.2%
 
121.840.2%
 
13040.2%
 
23.440.2%
 
101.140.2%
 
51.130.2%
 
Other values (1372)188494.7%
 
ValueCountFrequency (%) 
6.420.1%
 
6.810.1%
 
6.910.1%
 
7.610.1%
 
7.720.1%
 
7.820.1%
 
7.910.1%
 
810.1%
 
8.110.1%
 
8.310.1%
 
ValueCountFrequency (%) 
1635.110.1%
 
144810.1%
 
1400.610.1%
 
1266.410.1%
 
123910.1%
 
1235.610.1%
 
1229.610.1%
 
1215.610.1%
 
1213.510.1%
 
1186.710.1%
 

Aggravated assault
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count1628
Unique (%)81.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean281.85047762694825
Minimum31.3
Maximum1557.6
Zeros0
Zeros (%)0.0%
Memory size15.7 KiB
2020-09-30T12:26:02.512734image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum31.3
5-th percentile79.36
Q1163.6
median248.7
Q3366.2
95-th percentile562.4
Maximum1557.6
Range1526.3
Interquartile range (IQR)202.6

Descriptive statistics

Standard deviation163.7407747
Coefficient of variation (CV)0.5809490765
Kurtosis6.437606776
Mean281.8504776
Median Absolute Deviation (MAD)95.6
Skewness1.674565353
Sum560600.6
Variance26811.04129
2020-09-30T12:26:02.806943image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
205.150.3%
 
24240.2%
 
218.440.2%
 
157.240.2%
 
473.240.2%
 
256.830.2%
 
219.430.2%
 
242.730.2%
 
222.630.2%
 
195.830.2%
 
138.530.2%
 
137.630.2%
 
205.930.2%
 
249.230.2%
 
213.730.2%
 
19830.2%
 
298.730.2%
 
205.730.2%
 
163.130.2%
 
145.930.2%
 
425.530.2%
 
12530.2%
 
365.830.2%
 
218.530.2%
 
338.730.2%
 
Other values (1603)190895.9%
 
ValueCountFrequency (%) 
31.310.1%
 
31.610.1%
 
31.710.1%
 
32.310.1%
 
34.110.1%
 
35.610.1%
 
3820.1%
 
38.410.1%
 
38.510.1%
 
41.310.1%
 
ValueCountFrequency (%) 
1557.610.1%
 
1454.710.1%
 
1441.810.1%
 
1304.710.1%
 
1162.110.1%
 
1121.410.1%
 
111710.1%
 
1075.210.1%
 
956.110.1%
 
94310.1%
 

property total
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count1940
Unique (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4014.4783308195074
Minimum1524.4
Maximum9512.1
Zeros0
Zeros (%)0.0%
Memory size15.7 KiB
2020-09-30T12:26:03.091145image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1524.4
5-th percentile2197
Q13043.1
median3919.5
Q34758.2
95-th percentile6332.14
Maximum9512.1
Range7987.7
Interquartile range (IQR)1715.1

Descriptive statistics

Standard deviation1270.984536
Coefficient of variation (CV)0.3166001736
Kurtosis0.5015981608
Mean4014.478331
Median Absolute Deviation (MAD)859.7
Skewness0.6778136202
Sum7984797.4
Variance1615401.692
2020-09-30T12:26:03.374346image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4499.820.1%
 
4549.920.1%
 
3975.720.1%
 
3870.720.1%
 
2853.120.1%
 
3370.820.1%
 
487820.1%
 
4019.520.1%
 
2448.320.1%
 
4722.720.1%
 
284520.1%
 
2624.220.1%
 
2546.820.1%
 
3898.920.1%
 
4808.820.1%
 
3431.520.1%
 
2581.120.1%
 
2546.320.1%
 
5206.720.1%
 
282120.1%
 
4083.520.1%
 
4287.220.1%
 
3738.420.1%
 
210320.1%
 
2075.820.1%
 
Other values (1915)193997.5%
 
ValueCountFrequency (%) 
1524.410.1%
 
1718.210.1%
 
1724.310.1%
 
1734.110.1%
 
1736.810.1%
 
1766.410.1%
 
176710.1%
 
1769.910.1%
 
1780.210.1%
 
1820.710.1%
 
ValueCountFrequency (%) 
9512.110.1%
 
9426.910.1%
 
8839.310.1%
 
8574.210.1%
 
8422.610.1%
 
8402.810.1%
 
831610.1%
 
8314.710.1%
 
8287.610.1%
 
8151.510.1%
 

Burglary
Real number (ℝ≥0)

Distinct count1852
Unique (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean963.8274007038711
Minimum257.2
Maximum2906.7
Zeros0
Zeros (%)0.0%
Memory size15.7 KiB
2020-09-30T12:26:03.661049image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum257.2
5-th percentile399.04
Q1632.2
median908.4
Q31198.3
95-th percentile1788.9
Maximum2906.7
Range2649.5
Interquartile range (IQR)566.1

Descriptive statistics

Standard deviation428.554019
Coefficient of variation (CV)0.4446377212
Kurtosis0.8463519762
Mean963.8274007
Median Absolute Deviation (MAD)281.1
Skewness0.9061907813
Sum1917052.7
Variance183658.5472
2020-09-30T12:26:03.904722image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1509.530.2%
 
709.130.2%
 
405.430.2%
 
811.330.2%
 
546.230.2%
 
1013.820.1%
 
546.920.1%
 
863.820.1%
 
979.320.1%
 
663.520.1%
 
816.920.1%
 
915.620.1%
 
891.820.1%
 
1213.620.1%
 
621.920.1%
 
1603.920.1%
 
1075.620.1%
 
502.520.1%
 
973.520.1%
 
1075.920.1%
 
841.720.1%
 
332.420.1%
 
1121.220.1%
 
1154.120.1%
 
650.720.1%
 
Other values (1827)193497.2%
 
ValueCountFrequency (%) 
257.210.1%
 
277.710.1%
 
286.610.1%
 
289.110.1%
 
296.510.1%
 
307.910.1%
 
309.210.1%
 
309.310.1%
 
313.710.1%
 
320.610.1%
 
ValueCountFrequency (%) 
2906.710.1%
 
2820.410.1%
 
2727.310.1%
 
2659.210.1%
 
2646.510.1%
 
2559.710.1%
 
2506.810.1%
 
2453.110.1%
 
2412.710.1%
 
2392.510.1%
 

Larceny theft
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count1896
Unique (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2674.4768225238813
Minimum1160.8
Maximum5833.8
Zeros0
Zeros (%)0.0%
Memory size15.7 KiB
2020-09-30T12:26:04.158903image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1160.8
5-th percentile1548.84
Q12112.7
median2623.5
Q33091.9
95-th percentile4157.74
Maximum5833.8
Range4673
Interquartile range (IQR)979.2

Descriptive statistics

Standard deviation771.9775622
Coefficient of variation (CV)0.2886461964
Kurtosis0.2532826518
Mean2674.476823
Median Absolute Deviation (MAD)492.9
Skewness0.6183686596
Sum5319534.4
Variance595949.3565
2020-09-30T12:26:04.416085image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2714.330.2%
 
215630.2%
 
2422.730.2%
 
2404.120.1%
 
2568.320.1%
 
2693.320.1%
 
2759.320.1%
 
1927.320.1%
 
1819.620.1%
 
2964.320.1%
 
2319.320.1%
 
1880.420.1%
 
3080.720.1%
 
2834.420.1%
 
260720.1%
 
2500.520.1%
 
2271.320.1%
 
324620.1%
 
331320.1%
 
1715.320.1%
 
3101.620.1%
 
1776.520.1%
 
1987.220.1%
 
3082.920.1%
 
2741.820.1%
 
Other values (1871)193697.3%
 
ValueCountFrequency (%) 
1160.810.1%
 
1235.610.1%
 
1239.410.1%
 
1239.910.1%
 
1248.310.1%
 
127310.1%
 
1276.610.1%
 
1278.310.1%
 
1288.110.1%
 
1301.210.1%
 
ValueCountFrequency (%) 
5833.810.1%
 
5779.910.1%
 
544910.1%
 
5298.710.1%
 
5212.510.1%
 
5205.910.1%
 
5164.310.1%
 
5106.110.1%
 
5067.910.1%
 
5046.910.1%
 

vehicle theft
Real number (ℝ≥0)

Distinct count1707
Unique (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean376.17450980392164
Minimum38.9
Maximum1839.9
Zeros0
Zeros (%)0.0%
Memory size15.7 KiB
2020-09-30T12:26:04.680773image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum38.9
5-th percentile119.12
Q1210.1
median326.5
Q3473.3
95-th percentile812.24
Maximum1839.9
Range1801
Interquartile range (IQR)263.2

Descriptive statistics

Standard deviation232.377922
Coefficient of variation (CV)0.6177396819
Kurtosis4.860407211
Mean376.1745098
Median Absolute Deviation (MAD)125.7
Skewness1.737871073
Sum748211.1
Variance53999.49865
2020-09-30T12:26:05.002001image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
21150.3%
 
302.540.2%
 
237.640.2%
 
196.340.2%
 
153.540.2%
 
375.140.2%
 
601.730.2%
 
305.630.2%
 
273.330.2%
 
178.330.2%
 
183.430.2%
 
226.730.2%
 
212.730.2%
 
143.230.2%
 
173.730.2%
 
270.330.2%
 
351.430.2%
 
161.730.2%
 
272.930.2%
 
36630.2%
 
169.930.2%
 
430.130.2%
 
139.130.2%
 
301.630.2%
 
153.630.2%
 
Other values (1682)190795.9%
 
ValueCountFrequency (%) 
38.910.1%
 
53.810.1%
 
60.110.1%
 
64.610.1%
 
68.610.1%
 
70.110.1%
 
71.410.1%
 
72.210.1%
 
73.510.1%
 
74.510.1%
 
ValueCountFrequency (%) 
1839.910.1%
 
183710.1%
 
1776.510.1%
 
1686.510.1%
 
154810.1%
 
151710.1%
 
1449.310.1%
 
1430.810.1%
 
1394.810.1%
 
1392.410.1%
 

Interactions

2020-09-30T12:24:56.820882image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:24:57.290293image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:24:57.691577image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:24:58.091862image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:24:58.511158image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:24:58.917946image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:24:59.267196image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:24:59.663976image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:00.059757image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:00.493064image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:00.870834image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:01.260108image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:01.663896image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-09-30T12:25:44.269168image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:44.658944image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-09-30T12:25:47.026142image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:47.465953image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:47.823707image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:48.382105image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:48.834925image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:49.248219image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:49.654507image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:50.030775image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:50.391042image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:50.745293image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:51.113054image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:51.566876image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:51.950649image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:52.346431image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:52.754719image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:53.129985image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:53.490240image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:53.919045image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:54.347348image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:54.735125image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:55.101884image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:55.473740image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-09-30T12:26:05.495351image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-30T12:26:06.107285image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-30T12:26:06.673687image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-30T12:26:07.264107image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-09-30T12:25:56.229776image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-09-30T12:25:57.088887image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

stateunemploymentyearPopulationviolent totalMurderrapeRobberyAggravated assaultproperty totalBurglaryLarceny theftvehicle theft
0AL6.80819763665000388.815.121.796.0256.03419.51170.01987.2262.3
1AL7.32519773690000414.414.225.296.8278.33298.21135.51881.9280.7
2AL6.38019783742000419.113.325.599.1281.23519.71229.31987.9302.5
3AL7.15819793769000413.313.227.5109.5263.13830.51287.32223.2320.1
4AL8.86719803861466448.513.230.0132.1273.24485.11526.72642.2316.2
5AL10.54219813916000470.511.926.1126.5306.14428.31450.72693.3284.2
6AL14.10019823943000447.710.626.0112.0299.14185.81256.22656.4273.3
7AL13.80819833959000416.09.223.598.4284.93685.01073.12381.4230.5
8AL11.00819843990000431.29.425.196.1300.63470.91001.82235.5233.6
9AL9.16719854021000457.59.826.8105.4315.53484.61034.92191.2258.5

Last rows

stateunemploymentyearPopulationviolent totalMurderrapeRobberyAggravated assaultproperty totalBurglaryLarceny theftvehicle theft
1979WY3.5832005508798230.02.824.015.3188.33158.0476.82536.0145.0
1980WY3.1752006515004253.82.529.314.0208.02986.6450.92379.6156.1
1981WY2.8172007522830257.14.032.916.1204.12879.1452.32271.3155.5
1982WY3.0502008532668249.72.334.716.3196.42725.0412.62174.7137.6
1983WY6.3252009544270219.72.031.614.3171.82616.9399.82078.0139.1
1984WY6.4582010564554197.91.428.713.6154.12456.6381.01970.8104.9
1985WY5.8082011567356219.43.225.712.5178.02269.8328.51849.591.8
1986WY5.3172012576626201.32.426.710.6161.62293.0368.51823.2101.3
1987WY4.7252013583223207.82.924.712.7157.22196.2335.41761.899.1
1988WY4.1582014584153195.52.721.69.1153.91964.7289.11572.4103.2